On 3/4/2020 5:25 PM, Bruce Kellett wrote:
On Thu, Mar 5, 2020 at 11:59 AM 'Brent Meeker' via Everything List <everything-list@googlegroups.com <mailto:everything-list@googlegroups.com>> wrote:

    On 3/4/2020 4:34 PM, Bruce Kellett wrote:

    The crux of the matter is that all branches are equivalent when
    both outcomes occur on every trial, so all observers will infer
    that their observed relative frequencies reflect the actual
    probabilities. Since there are observers for all possibilities
    for p in the range [0,1], and not all can be correct, no sensible
    probability value can be assigned to such duplication experiments.

    The problem is even worse in quantum mechanics, where you measure
    a state such as

         |psi> = a|0> + b|1>.

    When both outcomes occur on every trial, the result of a sequence
    of N trials is all possible binary strings of length N, (all 2^N
    of them). You then notice that this set of all possible strings
    is obtained whatever non-zero values of a and b you assume. The
    assignment of some propbability relation to the coefficients is
    thus seen to be meaningless -- all probabilities occur equal for
    any non-zero choices of a and b.

    But  E(number|0>) = aN


Where does this come from? The weight of each branch is a^x*b^y for a branch with x zeros and y ones. But this weight is external to the branch, and the 1p probability estimates from within the branch are necessarily independent of the overall coefficient. The expectation for the number of zeros within any branch depends on the branch, but is independent of both a and b.

Sorry, I see I didn't make it clear I was assuming the Born rule.  I was just pointing out that this makes an assignment of probabilities to the multiple worlds which is the same as looking at a single world as a member of an ensemble.

Brent

I suspect that you are mixing the 1p and 3p viewpoints. Or else you are using the expectation for a single outcome per trial (not that for which both outcomes occur on every trial.)

Bruce


      and Var(number|0>) = abN.  The fraction x within one
    std-deviation of the expected number is a constant

        F( a-sqrt[ab/N]<x<a+sqrt[ab/N])=1/e

    So that fraction become more an more sharply confined around a as
    N->oo.

    Brent

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